9 papers
A Local-Linearly Convergent Algorithm for Nonconvex Equality-Constrained Optimization
Frank E. Curtis, Lingjun Guo, Daniel P. Robinson
For solving nonconvex equality-constrained optimization problems, a recent Gradient-Eigenstep Algorithm by Goyens et al.~is an iteration-efficient approach, based on minimizing Fle…
Robust Server Defense Against Unreliable Clients in One-Shot Fair Collaborative Machine Learning
Chia-Yuan Wu, Frank E. Curtis, Daniel P. Robinson
Collaborative machine learning (CML) enables multiple clients to train a global model jointly in a data-distributed setting. To address data privacy and communication efficiency, o…
A Proximal-Gradient Method for Solving Regularized Optimization Problems with General Constraints
Frank E. Curtis, Xiaoyi Qu, Daniel P. Robinson
We propose, analyze, and test a proximal-gradient method for solving regularized optimization problems with general constraints. The method employs a decomposition strategy to comp…
Progressively Sampled Equality-Constrained Optimization
Frank E. Curtis, Lingjun Guo, Daniel P. Robinson
An algorithm is proposed, analyzed, and tested for solving continuous nonlinear-equality-constrained optimization problems where the objective and constraint functions are defined…
Active-Set Identification in Noisy and Stochastic Optimization
Frank E. Curtis, Daniel P. Robinson, Lara Zebiane
Identifying active constraints from a point near a local minimizer is important both theoretically and practically in constrained continuous optimization as it can help identify op…
Fair Supervised Learning Through Constraints on Smooth Nonconvex Unfairness-Measure Surrogates
Zahra Khatti, Daniel P. Robinson, Frank E. Curtis
A new strategy for fair supervised machine learning is proposed. The main advantages of the proposed strategy as compared to others in the literature are as follows. (a) We introdu…
Using Synthetic Data to Mitigate Unfairness and Preserve Privacy in Collaborative Machine Learning
Chia-Yuan Wu, Frank E. Curtis, Daniel P. Robinson
In distributed computing environments, collaborative machine learning enables multiple clients to train a global model collaboratively. To preserve privacy in such settings, a comm…
A Self-Correcting Variable-Metric Algorithm Framework for Nonsmooth Optimization
Frank E. Curtis, Daniel P. Robinson, Baoyu Zhou
An algorithm framework is proposed for minimizing nonsmooth functions. The framework is variable-metric in that, in each iteration, a step is computed using a symmetric positive de…
An Inexact Regularized Newton Framework with a Worst-Case Iteration Complexity of for Nonconvex Optimization
Frank E. Curtis, Daniel P. Robinson, Mohammadreza Samadi
An algorithm for solving smooth nonconvex optimization problems is proposed that, in the worst-case, takes iterations to drive the norm of the gradient of t…